createDataPartition
Data Splitting functions
A series of test/training partitions are created using
createDataPartition while createResample creates one or more
bootstrap samples. createFolds splits the data into k groups
while createTimeSlices creates cross-validation split for series data.
groupKFold splits the data based on a grouping factor.
- Keywords
- utilities
Usage
createDataPartition(
y,
times = 1,
p = 0.5,
list = TRUE,
groups = min(5, length(y))
)createFolds(y, k = 10, list = TRUE, returnTrain = FALSE)
createMultiFolds(y, k = 10, times = 5)
createTimeSlices(y, initialWindow, horizon = 1, fixedWindow = TRUE, skip = 0)
groupKFold(group, k = length(unique(group)))
createResample(y, times = 10, list = TRUE)
Arguments
- y
a vector of outcomes. For
createTimeSlices, these should be in chronological order.- times
the number of partitions to create
- p
the percentage of data that goes to training
- list
logical - should the results be in a list (
TRUE) or a matrix with the number of rows equal tofloor(p * length(y))andtimescolumns.- groups
for numeric
y, the number of breaks in the quantiles (see below)- k
an integer for the number of folds.
- returnTrain
a logical. When true, the values returned are the sample positions corresponding to the data used during training. This argument only works in conjunction with
list = TRUE- initialWindow
The initial number of consecutive values in each training set sample
- horizon
the number of consecutive values in test set sample
- fixedWindow
logical, if
FALSE, all training samples start at 1- skip
integer, how many (if any) resamples to skip to thin the total amount
- group
a vector of groups whose length matches the number of rows in the overall data set.
Details
For bootstrap samples, simple random sampling is used.
For other data splitting, the random sampling is done within the levels of
y when y is a factor in an attempt to balance the class
distributions within the splits.
For numeric y, the sample is split into groups sections based on
percentiles and sampling is done within these subgroups. For
createDataPartition, the number of percentiles is set via the
groups argument. For createFolds and createMultiFolds,
the number of groups is set dynamically based on the sample size and
k. For smaller samples sizes, these two functions may not do
stratified splitting and, at most, will split the data into quartiles.
Also, for createDataPartition, very small class sizes (<= 3) the
classes may not show up in both the training and test data
For multiple k-fold cross-validation, completely independent folds are
created. The names of the list objects will denote the fold membership
using the pattern "Foldi.Repj" meaning the ith section (of k) of the jth
cross-validation set (of times). Note that this function calls
createFolds with list = TRUE and returnTrain = TRUE.
Hyndman and Athanasopoulos (2013)) discuss rolling forecasting origin
techniques that move the training and test sets in time.
createTimeSlices can create the indices for this type of splitting.
For Group k-fold cross-validation, the data are split such that no group
is contained in both the modeling and holdout sets. One or more group
could be left out, depending on the value of k.
Value
A list or matrix of row position integers corresponding to the
training data. For createTimeSlices subsamples are named by the end
index of each training subsample.
References
http://topepo.github.io/caret/data-splitting.html
Hyndman and Athanasopoulos (2013), Forecasting: principles and practice. https://www.otexts.org/fpp
Examples
# NOT RUN {
data(oil)
createDataPartition(oilType, 2)
x <- rgamma(50, 3, .5)
inA <- createDataPartition(x, list = FALSE)
plot(density(x[inA]))
rug(x[inA])
points(density(x[-inA]), type = "l", col = 4)
rug(x[-inA], col = 4)
createResample(oilType, 2)
createFolds(oilType, 10)
createFolds(oilType, 5, FALSE)
createFolds(rnorm(21))
createTimeSlices(1:9, 5, 1, fixedWindow = FALSE)
createTimeSlices(1:9, 5, 1, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = FALSE)
createTimeSlices(1:15, 5, 3)
createTimeSlices(1:15, 5, 3, skip = 2)
createTimeSlices(1:15, 5, 3, skip = 3)
set.seed(131)
groups <- sort(sample(letters[1:4], size = 20, replace = TRUE))
table(groups)
folds <- groupKFold(groups)
lapply(folds, function(x, y) table(y[x]), y = groups)
# }